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Charles Elkan

person1 events
papersTODAY 04:00 UTC

Paper analyzes top-K ranking errors caused by class weighting in one-vs-rest models

A new arXiv preprint examines how a standard remedy for class imbalance — assigning each label a positive-class weight equal to the ratio of negatives to positives — alters the ranking of the top K items in one-vs-rest rankers. Drawing on Elkan's identity, which shows the weight shifts a label's log-odds, the authors characterize the resulting slippage in top-K results and propose a way to diagnose and repair those errors.

arxiv/ranking/class-imbalance/recommender-systems